Principal AI Engineer

About Velsera

Medicine moves too slow. At Velsera, we are changing that.

Velsera was formed in 2023 through the shared vision of Seven Bridges and Pierian, with a mission to accelerate the discovery, development, and delivery of life-changing insights.

Velsera provides software and professional services for:

• AI-powered multimodal data harmonization and analytics for drug discovery and development
• IVD development, validation, and regulatory approval
• Clinical NGS interpretation, reporting, and adoption

With our headquarters in Boston, MA, we are growing and expanding our teams located in different countries!

About the role 

Velsera’s Seven Bridges Platform is used by biomedical researchers and pharma teams to run reproducible analyses in regulated environments. We’re adding an AI platform layer to Seven Bridges—model invocation, self-hosted LLM serving, governance, and workflow integration—without compromising security, auditability, or interoperability. 

You’ll report to the CTO as a senior individual contributor. You’ll design and ship production AI systems that meet compliance needs (e.g., FedRAMP, HIPAA/21 CFR Part 11/GxP), work across AWS, Azure and GCP, and set the technical direction for what will grow into an AI platform team. 

• Build a governed model access layer (self-hosted open-weight models, cloud-managed models such as Bedrock, and customer-supplied models) 

• Integrate AI capabilities into platform experiences (batch workflows and interactive sessions) 

• Establish patterns for evaluation, versioning, approvals, audit trails, and safe rollout 

• Partner with product, security/compliance, and scientific teams to introduce AI-native architectures and ship capabilities customers can adopt 

This role is a fit if:

• You want to build the platform layer (serving, governance, integrations)—not do model research or purely prompt engineering. 

• You’re excited about shipping in regulated environments where auditability and access control are core requirements. 

• You like working inside an existing production platform and improving it without breaking what customers rely on. 

What will you do?

• A production-ready, compliant AI/LLM serving and invocation layer for Seven Bridges (multi-tenant, auditable, and secure) 

• A clear governance workflow for models (intake, evaluation, approval, versioning, deprecation) that works for regulated customers 

• A first set of “AI in the platform” features shipped end-to-end (e.g., assisted validation/compliance tooling, cost/error assistance, workflow helpers) 

• Integration patterns that keep workflows reproducible and standards-aligned (CWL/WDL/Nextflow and GA4GH-friendly where applicable) 

• Operational readiness: monitoring, incident playbooks, and measurable SLOs for key AI services 

How we build (and what we’ll expect you to optimize for):

You’ll make trade-offs in a platform that’s standards-driven, multi-cloud, and compliance-heavy. A few things matter a lot here: 

• Standards and interoperability. Prefer open standards and clean interfaces over one-off integrations. 

• Multi-cloud reality. Design for AWS and GCP; avoid hard dependencies on a single provider’s AI stack. 

• Security/auditability by default. Access control, logging, traceability, and data governance are part of the design—not add-ons. 

• Reproducibility. AI features should fit into workflows that need to be repeatable and explainable. 

Requirements

What do you bring to the table?

• 7+ years in software engineering, including 3+ years shipping AI/ML systems to production 

• Strong Python, plus one of Java/Go/TypeScript; comfortable in a polyglot codebase and production code reviews 

• Hands-on experience with secure cloud architectures on AWS (network isolation, IAM boundaries, private connectivity, audit logging) 

• Experience operating or integrating model serving across options: self-hosted open-weight models, managed model APIs (e.g., Bedrock), and customer-provided models 

• MLOps experience using AWS Bedrock, Google Vertex AI or similar 

• Built governance for ML/LLM systems (evaluation, versioning, approvals, rollout/rollback, deprecation) 

• Comfortable designing for regulated environments (FedRAMP, HIPAA, 21 CFR Part 11, GxP, or similar) 

• Experience with RAG and LLM tool-use/agentic patterns beyond prototypes 

• Clear written communication for mixed audiences (engineering, product, security/compliance, and scientists) 

Nice-to-have:

• Experience in genomics, biomedical data, or life sciences platforms 

• Integrating AI capabilities into workflow engines (CWL/WDL/Nextflow) or similar orchestration systems 

• Familiarity with GA4GH standards (e.g., WES/DRS/TRS) and/or clinical data models (FHIR/OMOP) 

• Production experience on both AWS and GCP; comfort making pragmatic multi-cloud trade-offs 

Benefits

• Flexible Work & Time Off - Embrace hybrid work models and enjoy the freedom of unlimited paid time off to support work-life balance.
• Health & Well-being - Access comprehensive group medical and life insurance coverage, along with a 24/7 Employee Assistance Program (EAP) for mental health and wellness support.
• Growth & Learning - Fuel your professional journey with continuous learning and development programs designed to help you upskill and grow.
• Engaging & Fun Work Culture - Experience a vibrant workplace with team events, celebrations, and engaging activities that make every workday enjoyable.
• & Many More...